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arXiv · 2409.12620

Accurate Automatic 3D Annotation of Traffic Lights and Signs for Autonomous Driving

Sándor Kunsági-Máté·Levente Pető·Lehel Seres·Tamás Matuszka
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Abstract

3D detection of traffic management objects, such as traffic lights and road signs, is vital for self-driving cars, particularly for address-to-address navigation where vehicles encounter numerous intersections with these static objects. This paper introduces a novel method for automatically generating accurate and temporally consistent 3D bounding box annotations for traffic lights and signs, effective up to a range of 200 meters. These annotations are suitable for training real-time models used in self-driving cars, which need a large amount of training data. The proposed method relies only on RGB images with 2D bounding boxes of traffic management objects, which can be automatically obtained using an off-the-shelf image-space detector neural network, along with GNSS/INS data, eliminating the need for LiDAR point cloud data.

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Sándor Kunsági-Máté, Levente Pető, Lehel Seres, Tamás Matuszka. 2024-09-19. Accurate Automatic 3D Annotation of Traffic Lights and Signs for Autonomous Driving. https://arxiv.org/abs/2409.12620

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